Browse Articles

Daily transient analysis of an integrated solar-driven direct contact membrane distillation for cogeneration production of freshwater and electricity

Spin-selective heterogeneous chiral perovskites for circular-polarization-resolved retinomorphic sensors

Dvalue of serum biomarkers for identifying computed tomography abnormalities in male miners

AI-guided multi-omics analysis identifies NPC1-modulated susceptibility to SARS-CoV-2 infection under PM2.5 exposure

Production, characterization, and functional properties of protein concentrate from Momordica cochinchinensis seeds

Direct observation of cation-dependent polarisation switching dynamics in fluorite ferroelectrics

Integrated experimental and computational evaluation of Anagallis foemina derived terpenoids against carbapenem resistant Acinetobacter baumannii

Domain gain or loss in a fungal chitinase enables specialization towards antagonism or immune suppression

Abstract Effector proteins orchestrate interactions that determine host compatibility and microbial competition. Among these are microbial chitinases, widespread enzymes involved in nutrient acquisition, fungal cell-wall remodelling, and antagonism, yet how their functions diversify across ecological roles remains unclear. Here, we show that modular domain variation within a conserved GH18 chitinase family enables specialization between microbial antagonism and host immune suppression in the beneficial root endophyte Serendipita indica . The chitinase Si CHIT, bearing a C-terminal CBM5 domain, is induced during fungal confrontation and inhibits growth of the phytopathogen Bipolaris sorokiniana , thereby protecting host roots. Deleting CBM5 abolishes this antagonistic activity, whereas grafting CBM5 onto its paralog Si CHIT2, which lacks this domain, confers antifungal function. In contrast, Si CHIT2 is induced during root colonization and suppresses chitin-triggered immunity, promoting host compatibility. These results show how domain loss and regulatory divergence can reprogram antimicrobial enzymes into immune-suppressive effectors in beneficial plant-associated fungi.

Can classification strategies improve automated cervical vertebral maturation staging? A comparative study

Abstract Accurate assessment of skeletal maturity is essential in determining orthodontic treatment timing. Cervical vertebral maturation (CVM) staging is commonly used, but inter-observer variability remains a major obstacle. Recently, artificial intelligence (AI) has been used to provide more consistent and faster radiographic assessments. This study aimed to compare various deep learning approaches and investigate how different training strategies impact model performance in automated CVM staging. A total of 1,750 lateral cephalometric radiographs were independently evaluated and labeled by two board-certified orthodontists, with discrepancies resolved by consensus. This dataset was then divided into a training set ( n  = 1,600; stratified 5-fold cross-validation) and a held-out test set ( n  = 150). First, we compared the end-to-end 6-stage model (LS6) with landmark-guided 6-stage models (LM6_1 and LM6_2, collectively referred to as LM6) to test the effect of structural priors. Then, we evaluated a fine-to-coarse 3-stage model (LS6_3), trained on 6 stages and then aggregated for 3-stage classification, against a direct 3-stage model (LS3) to assess the impact of label granularity. For 6-stage classification, LS6 achieved 67.3% accuracy (κ = 0.912), outperforming LM6_1 (58.8%) and LM6_2 (64.4%). Tolerance-based analysis demonstrated high ± 1-stage accuracy for all models, with LS6 achieving 94.3% and LM6 models achieving 92.9% (LM6_1) and 92.8% (LM6_2), confirming that misclassifications were predominantly between adjacent stages. For 3-stage classification, LS6_3 (79.3%) showed slightly higher accuracy than LS3 (78.8%). In the Grad-CAM analysis, LS6_3 showed a more concentrated focus on key vertebral features compared to LS3. These findings suggest that landmark-based priors may not necessarily enhance CVM staging performance, and that fine-grained training encourages more anatomically focused feature learning. These results provide preliminary evidence for optimizing AI training protocols in skeletal maturity assessment.

Rare jackpot individuals drive rapid adaptation in Threespine Stickleback

Abstract Recombination has long been considered the primary mechanism to bring beneficial alleles together, which can increase the speed of adaptation from standing genetic variation. Recombination is fundamental to the transporter hypothesis proposed to explain precise parallel adaptation in Threespine Stickleback. We study an instance of freshwater adaptation in the Threespine Stickleback system using whole genome data from an evolutionary time-series to observe the genomic dynamics underlying rapid parallel adaptation. Here, we show that rapid adaptation to a freshwater environment depends on a few individuals with large haploblocks of freshwater-adaptive alleles (jackpot carriers) present among the anadromous founders at low frequencies. Biological kinship analyses indicates that mating among jackpot carriers and between jackpot carriers and non-jackpot individuals led to an increase in freshwater-adaptive alleles within the first few generations. This process allowed the population to overcome a substantial bottleneck likely caused by the low fitness of first-generation stickleback possessing a few freshwater-adaptive alleles born in the lake. Additionally, we find evidence that the genetic load that emerged from population growth after the bottleneck may have been reduced through an increase in homozygosity by inbreeding, ultimately purging deleterious alleles. Recombination likely played a limited role in this case of very rapid adaptation.

Prognostic impact of acute kidney injury on one-year survival in patients undergoing high-risk PCI with Impella support

Targeting notch signaling to restore neural development and behavior in mouse models of ASD

Determinants of healthcare utilisation among post-COVID syndrome patients: a population-based study in Malaysia

Lipid turnover and GEF recruitment collectively determine Rab5 recruitment and activation during the first step of early endosome formation

Enhancing SPR biosensor performance for creatinine detection via plasma polymerized heptylamine coatings

Structural basis for the assembly and translocation of the Vip1-Vip2 insecticidal toxin from Bacillus thuringiensis

Gastroenterological disease detection using transformer-based medical imaging for sustainable healthcare

Abstract Early detection of gastroenterological diseases significantly improves patient outcomes and reduces late-stage diagnostic burden, yet traditional CNN models show limitations in capturing complex patterns within medical imaging datasets, prompting investigation into transformer architectures like Vision Transformer (ViT). Application of the ViT technology in detecting gastroenterological diseases with the help of medical imaging has not been fully explored, despite the promising capabilities. In this paper, the effectiveness of the ViT-B16 structure for the identification of gastrointestinal abnormalities is considered using a combined dataset of Curated Colon Dataset and HyperKvasir Dataset (10,000 images across four classes), and compared with established methodologies. Our experimental results showed that ViT-B16 performed better when compared to alternative approaches; it achieved 99.5% classification accuracy compared to 99.1% by EfficientNetB5 and 97.1% by EfficientNetB2, with other supportive performance metrics including precision (99.4%), recall (99.4%), and F1-score (99.4%), AUC values ranged from 0.99 to 1.00 across all classes, reflecting very strong discriminatory power regarding disease classification tasks. These suggest that ViT-B16 has great potential for medical diagnosis applications, especially classification tasks in healthcare, where evidence-based decision-making and model interpretability are key considerations. The model also supports sustainable healthcare through computational efficiency and reduced diagnostic burden. However, there are several challenges that have not been addressed, including addressing ethical concerns about diagnostics, improving diagnostic accuracy for underrepresented disease classes, and validating the model across diverse clinical settings, which are essential directions for future research to continue developing gastroenterological disease-detecting techniques.

Pathogenic variants in the cohesin loader subunit MAU2 underlie a distinct Cornelia de Lange Syndrome subtype

Abstract The role of the cohesin complex depends on the cohesin loader proteins NIPBL and MAU2. While NIPBL variants are a major cause of Cornelia de Lange Syndrome (CdLS), the role of MAU2 in disease is unclear. We describe 18 individuals carrying 15 heterozygous MAU2 variants and demonstrate pathogenicity through functional analyses. In-frame MAU2 variants predominantly impair NIPBL–MAU2 interaction, whereas truncating variants cause MAU2 haploinsufficiency and lead to NIPBL reduction. Most individuals exhibit a DNA methylation profile compatible with the CdLS episignature. We also describe two MAU2 -specific episignatures that reflect variant-dependent molecular consequences. Affected individuals display a wide range of phenotypes, from classic CdLS to milder presentations, with short stature and microcephaly as major features. A heterozygous Mau2 knockout mouse model recapitulates these traits, confirming the causal role of MAU2 disruption in vivo. Our study establishes MAU2 as a CdLS-associated gene and delineates a MAU2 -related chromatinopathy with variable expressivity.

Geospatial assessment of habitat degradation and climate impacts on migratory crane habitat in Pakistan’s Wetland ecosystems

Abstract Migratory cranes are ecologically significant avian species that depend on dynamic wetland ecosystems across their flyways. However, their habitats are increasingly threatened by anthropogenic pressures and environmental change. This study investigates the spatial and temporal dynamics of habitat suitability for the Demoiselle Crane ( Anthropoides virgo ) and Eurasian Crane ( Grus grus ) in Pakistan using multi-decadal geospatial datasets and remote sensing techniques. Supervised classification of Landsat imagery revealed significant land use/land cover (LULC) changes from 1994 to 2024, including a sharp increase in built-up areas (+ 22.4%) and a notable decrease in vegetation cover (– 4.2%), indicating intensifying habitat fragmentation and ecological stress. Key vegetation and water indices NDVI, NDWI, MNDWI, and LSWI were analyzed to evaluate ecological conditions relevant to crane habitats. Annual NDVI time-series trends indicated vegetation degradation in the early 2000s, followed by recovery after 2014 due to large-scale afforestation initiatives. Surface water dynamics, derived from the Joint Research Centre Global Surface Water dataset, showed fluctuations in water occurrence, seasonality, and recurrence factors critical for crane roosting and foraging. Climatic analysis using NASA POWER data revealed rising temperatures and variable precipitation patterns, further affecting wetland health and habitat suitability. Land Surface Flow (LSF) mapping identified critical migratory flyways along the Kurram River and Lora Nala, reinforcing the ecological importance of this corridor. This research highlights the value of geospatial tools in monitoring migratory bird habitats and underscores the urgency of integrating spatial data into national conservation policies. By identifying priority conservation zones and tracking habitat change, this study offers critical insights for the sustainable management of Pakistan’s wetlands and the protection of migratory crane populations along the Flyway.

Global stalled tropical cyclones in a changing climate

Abstract Tropical cyclone (TC) stalling refers to a storm wandering within a relatively small region. When TC stalling occurs, localized accumulated damage can increase substantially. However, the understanding of this special behavior globally, especially its response to climate warming, remains limited. Here, we provide a comprehensive global analysis of TC stalling and its response to climate warming, utilizing both observational data and climate model simulations. Our results reveal a distinct hemispheric asymmetry, showing that basins in the Southern Hemisphere are more prone to TC stalling than those in the Northern Hemisphere. Although a warming climate reduces the global probability of TC stalling occurrence, it significantly increases the daily rainfall by these storms, particularly over land and nearshore regions. Our analysis also indicates that, although the main drivers for the stalling vary in different basins, in general, they are mainly influenced by the steering wind vector (magnitude and direction) and TC location. Furthermore, changes in probability of TC stalling in climate warming are mainly affected by changes in the probability of TC exposure to a weak steering flow.